01Introduction
Blind Spots in a 200-Microservice Architecture
02Problem Statement
- Fintech platform with 200+ microservices, 4 Kubernetes clusters across AWS regions
- 3 P1 incidents in 90 days caused by undetected cascading failures
- No distributed tracing; engineers spending 4+ hours per incident on root cause
- Challenge: implement full-stack observability without service disruption
- Goal: MTTR under 15 minutes; SLO visibility for every customer-facing service
03Proposed Solution
Phase 1
Unified Observability: Metrics + Traces + Logs + RUM
- Datadog Agent deployed via Helm chart on all 4 EKS clusters
- APM with distributed tracing across all 200 services using auto-instrumentation
- Log Management with parsing pipelines; correlated to traces via trace_id
- Synthetic monitoring for 40 critical user journeys; alerting on SLO breach
- Real User Monitoring (RUM) for frontend latency and JS error rates
Phase 2
SLO Framework & Alert Fatigue Elimination
- Defined 3 SLO tiers: Platinum (99.99%), Gold (99.9%), Silver (99.5%)
- Error budgets tracked in Datadog dashboards; burn rate alerts auto-page on-call
- Replaced 400+ noisy CloudWatch alarms with 60 meaningful Datadog monitors
- Network Performance Monitoring for service-to-service latency mapping
- Custom metrics pipeline for business KPIs (payments/sec, auth success rate)
04Technologies
- Datadog
- APMLog ManagementRUMSyntheticsNPMDashboardsSLOs
- Kubernetes
- EKSHelmDatadog Operator
- Languages
- Node.jsPythonGo (all auto-instrumented)
- Alerting
- PagerDuty integration with escalation policies
- IaC
- Terraform for Datadog monitor and dashboard provisioning